41 citations · 68 across the 13 of their papers we have counts for
5 papers · 1 filter
The Explanation Necessity for Healthcare AI
Michail Mamalakis, Héloïse de Vareilles, Graham Murray +2
Explainability is a critical factor in enhancing the trustworthiness and acceptance of artificial intelligence (AI) in healthcare, where decisions directly impact patient outcomes.…
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification
Michail Mamalakis, Héloïse de Vareilles, Shun-Chin Jim Wu +7
In the last decade, computer vision has witnessed the establishment of various training and learning approaches. Techniques like adversarial learning, contrastive learning, diffusi…
Solving the enigma: Enhancing faithfulness and comprehensibility in explanations of deep networks
Michail Mamalakis, Antonios Mamalakis, Ingrid Agartz +4
The accelerated progress of artificial intelligence (AI) has popularized deep learning models across various domains, yet their inherent opacity poses challenges, particularly in c…
Statistical Agnostic Regression: a machine learning method to validate regression models
Juan M Gorriz, J. Ramirez, F. Segovia +3
Regression analysis is a central topic in statistical modeling, aimed at estimating the relationships between a dependent variable, commonly referred to as the response variable, a…
Is K-fold cross validation the best model selection method for Machine Learning?
Juan M Gorriz, R. Martin Clemente, F Segovia +3
As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common a…